Where to download englishpcfg.ser.gz

Typed dependencies are otherwise known grammatical relations. This style of output is available only for English and Chinese. For more details, please refer to the Stanford Dependencies webpage and the Universal Dependencies v1 documentation. See also the current Universal Dependencies documentation , but we are yet to update to it.

Shift-reduce constituency parser As of version 3. Models for this parser are linked below. In version 3. The parser outputs typed dependency parses for English and Chinese. The models for this parser are included in the general Stanford Parser models package. The package includes a tool for scoring of generic dependency parses, in a class edu.

This tool measures scores for dependency trees, doing F1 and labeled attachment scoring. The included usage message gives a detailed description of how to use the tool. The current version of the parser requires Java 8 or later. You can also download an old version of the parser, version 1.

The parser also requires a reasonable amount of memory at least MB to run as a PCFG parser on sentences up to 40 words in length; typically around MB of memory to be able to parse similarly long typical-of-newswire sentences using the factored model. Source is included. The download is a MB zipped file mainly consisting of included grammar data files. If you unpack the zip file, you should have everything needed. Simple scripts are included to invoke the parser on a Unix or Windows system.

For another system, you merely need to similarly configure the classpath. The parser code is dual licensed in a similar manner to MySQL, etc. I want to give the parser a list of sentences, one per line, to parse. Use the -sentences option.

If you want to give the parser one sentence per line, include the option -sentences newline in your invocation of LexicalizedParser. The parser treats a filename as - as meaning to read from stdin and by default writes to stdout this can be changed with the -writeOutputFiles option.

Note: the tokenizer uses lookahead, so you will either need to close the input to get the last sentence parsed, or use another option like -sentences newline. From the commandline, if you give the option -tokenized , then the parser will assume white-space separated tokens, and use your tokenization as is. Of course, parsing will suffer unless your tokenization accurately matches the tokenization of the underlying treebank, for instance Penn Treebank tokenization.

A common occurrence is that your text is already correctly tokenized but does not escape characters the way the Penn Treebank does e. In this case, you can use the -tokenized option but also add the flag:. If calling the parser within your own program, the main parse methods take a List of words which should already be correctly tokenized and escaped before calling the parser.

You don't need to and cannot give the -tokenized option. If you have untokenized text, it needs to tokenized before parsing. You may use the parse method that takes a String argument to have this done for you or you may be able to use of classes in the process package, such as DocumentPreprocessor and PTBTokenizer for tokenization, much as the main method of the parser does. Or you may want to use your own tokenizer. Yes, you can.

However, for good results, you should make sure that you provide correctly tokenized input and use exactly the correct tag names. That is, the input must be tokenized and normalized exactly as the material in the treebank underlying the grammar is. Read the Javadocs for the main method of the LexicalizedParser class.

The relevant options are -sentences see above , -tokenized , -tokenizerFactory , -tokenizerMethod , and -tagSeparator. You could then give the parser input such as:. If you wish to work with POS-tagged text programmatically, then things are different. You pass to the parse method a List. If the items in this list implement HasTag , such as being of type TaggedWord or CoreLabel , and the tag value is not null , then the parser will use the tags that you provide.

You can use the DocumentPreprocessor class, as the main method does, to produce these lists, or you could use WhitespaceTokenizer followed by WordToTaggedWordProcessor. Either form of list will pass the tags to the parser.

Or you can do this with code that you write. Here's an example that very manually makes the List in question:. There are other constraints which can be added, but they have to be added programmatically.

There is a call, setConstraints, which you can make before using the LexicalizedParserQuery to run the parser. If you add a ParserConstraint object spanning a set of words, the parser will only produce parse trees which include that span of words as a constituent. In general, you will want to use ". It is also possible to specify constraints such as "NN JJ" to enforce that the parser uses either an NN or JJ, for example, but unfortunately there is a subtle and complicated bug in the code that enforces that.

In general, though, you should not use this part of the feature and simply use ". Not yet, but in the future, very possibly. Can I obtain multiple parse trees for a single input sentence?

Yes, for the PCFG parser only. They can be printed either as phrase structure trees or as typed dependencies in the usual way via the -outputFormat option, and each receives a score log probability.

The k best parses are extracted efficiently using the algorithm of Huang and Chiang This may be because the parser chose an incorrect structure for your sentence, or because the phrase structure annotation conventions used for training the parser don't match your expectations.

To make sure you understand the annotation conventions, please read the bracketing guidelines for the parser model that you're using, which are referenced above. Or it may be because the parser made a mistake. While our goal is to improve the parser when we can, we can't fix individual examples. The parser is just choosing the highest probability analysis according to its grammar.

This parser is in the space of modern statistical parsers whose goal is to give the most likely sentence analysis to a list of words. It does not attempt to determine grammaticality, though it will normally prefer a "grammatical" parse for a sentence if one exists. This is appropriate in many circumstances, such as when wanting to interpret user input, or dealing with conversational speech, web pages, non-native speakers, etc. For other applications, such as grammar checking, this is less appropriate.

One could attempt to assess grammaticality by looking at the probabilities that the parser returns for sentences, but it is difficult to normalize this number to give a useful "grammaticality" score, since the probability strongly depends on other factors like the length of the sentence, the rarity of the words in the sentence, and whether word dependencies in the sentence being tested were seen in the training data or not. How much memory do I need to parse long sentences?

The parser uses considerable amounts of memory. If you see a java. OutOfMemoryError , you either need to give the parser more memory or to take steps to reduce the memory needed. You give java more memory at the command line by using the -mx flag, for example -mxm. Memory usage by the parser depends on a number of factors: Memory usage expands roughly with the square of the sentence length. You may wish to set a -maxLength and to skip long sentences. The factored parser requires several times as much memory as just running the PCFG parser, since it runs 3 parsers.

The command-line version of the parser currently loads the whole of an input file into memory before parsing any of it. If your file is extremely large, splitting it into multiple files and parsing them sequentially will reduce memory usage.

A bit application requires more memory than a bit application Java uses lots of pointers. A larger grammar or POS tag set requires more memory than a smaller one. We have parsed sentences as long as words, but you need lots of RAM and patience. If "xy. How can I obtain just the results of the POS tagger for each word in a sentence?

You can use the -outputFormat wordsAndTags option. Note: if you want to tag a lot of text, it'd be much faster to use a dedicated POS tagger such as ours or someone else's , since this option has the parser parse the sentences and just not print the other information. There isn't a separate included tagger; the parser does POS tagging as part of parsing.

Can I just get your typed dependencies grammatical relations output from the trees produced by another parser? You can use the main method of EnglishGrammaticalStructure for English, or the corresponding class for Chinese. You can give it options like -treeFile to read in trees, and, say, -collapsed to output typedDependenciesCollapsed. For example, this command with appropriate paths will convert a Penn Treebank file to uncollapsed typed dependencies:.

Also, here is a sample Java class that you can download that converts from an input file of trees to typed dependencies. Fine print: There is one subtlety. The conversion code generally expects Penn Treebank style trees which have been stripped of functional tags and empty elements. The exception is that it gets value from the -TMP annotation on bare temporal NPs in order to recognize them as having temporal function tmod.

Without the temporal annotation, some simple temporals like today will still be recognized, but a bare temporal like last week in I left last week will be tagged as an object dobj. With the Stanford parser, you can get marking of temporal NPs in the tree output by giving the option -retainTmpSubcategories , either on the command line or by passing it to the setOptionFlags String[] method of the parser. See the javadoc for the main method of edu. This is an element of the dependency analysis we adopted.

It's not uncontroversial, and it could have been done differently, but we'll try to explain briefly why we did things the way we did. The general philosophy of the grammatical relations design is that main predicates should be heads and auxiliaries should not.

So, for the sentence Jill is singing , you will see nsubj singing, Jill. We feel that this is more useful for most semantic interpretation applications, because it directly connects the main predicate with its arguments, while the auxiliary is rendered as modifying the verb aux singing, is.

Most people seem to agree. Create a new folder 'jars' in my example. Place the extracted files into this jar folder: stanford-parser Again, extract the file called 'englishPCFG.

Remember the location where you extract this ser. When creating a StanfordParser instance, you can provide the model path as parameter. As both tools changes rather quickly and the API might look very different months later. Please treat the following answer as temporal and not an eternal fix. The way NLTK is interfacing the tool is through the call the Java tool through the command line interface. So it is advisable to update your NLTK package to v3. Note that as of NLTK v3.

Why not 64 bit? You need to download and extract all the necessary Stanford NLP tools. Setup the environment variables such that NLTK can find the relevant file path automatically. You have to set the following variables:. Add the appropriate Stanford NLP. Code snippets found in the following Stackoverflow questions might not work:. There is NO need to set the environment variables if you use the following method BUT when the API changes its parameter names, you will need to change accordingly.

As of NLTK v3. As of the current Stanford parser , the default output for the lexparser. The Overflow Blog. Podcast Explaining the semiconductor shortage, and how it might end. Does ES6 make JavaScript frameworks obsolete? Featured on Meta. Now live: A fully responsive profile. Related Hot Network Questions. Question feed. Stack Overflow works best with JavaScript enabled.